Big Data Management

Big Data Management is the organization or use of large volumes of structured and unstructured data belonging to an organization. Its allow a company to understand their customers better, develop new products and decision- making in business based on the analysis of large amounts of corporate data. A Cloud data management interface is the venue for using cloud services; it is the software that presents the visual display of tools for manipulating the data. It helps organizations for realizing cloud-based service and using them to their business transformation by increasing business agility, lowering costs, and reducing IT complexity.

External Data Management

The IoT allows objects to be sensed or controlled remotely across existing network infrastructure, creating opportunities for more direct integration of the physical world into computer-based systems, and resulting in improved efficiency, accuracy and economic benefit in addition to reduced human intervention.

Data Transformation

Data transformation can deliver your business the most powerful insights to improve service and products. The usual process involves converting documents, but data conversions sometimes involve the conversion of a program from one computer language to another to enable the program to run on a different platform. The usual reason for this data migration is the adoption of a new system that's totally different from the previous one.

Data Quality & Cleaning

A major benefit of Data Quality is increasing confidence and trust in enterprise data by making data quality more visible and more relevant to the business.

- Lowers costs by standardizing on a single platform, eliminating redundant data quality tools and slashing license and maintenance costs;

- Operate more efficiently by empowering the business to participate in data quality processes, reducing reliance on IT

- Enhance IT productivity with powerful business-IT collaboration tools and a common data quality project environment.

Benefit of Data Cleaning :

- Improve quality with the ability to standardize, validate and correct name and address data;

- Increase developer efficiency through a codeless development environment for both data cleansing and integration;

- Optimize runtime for data correction to provide high performance and scalability

Master Data Management

MDM fits your unique requirements. It can be deployed on-premises and in the cloud and is perfectly designed for the realities of the hybrid world. MDM is not just about match and merge. Your success relies heavily on comprehensiveness of the solution.

- Ability to acquire the data quickly, whether it is on-premises, in the cloud, and from third-party sources

- Ability to understand the patterns and vitiations and suggest required corrections

- Allow you to easily enrich master data from external data providers

- Create a trusted view and securely deliver data for both analytical and operational use case

These end-to end capabilities bring data quality, data integration, business process, management, security is key to customers who use our solution today for mission-critical requirements.

Impact Analysis

Data lineage provides the ability to discover the origins of an element of data and describes the sequence of jobs and transformations which have occurred up to the point of the request for the lineage information. Impact analysis is the reverse flow of information that can be used to trace the use and consumption of a data item, typically for the purpose of managing change or assessing and auditing access. Data governance refers to the general management of key data resources in a company or organization. This broad term encompasses elements of data use, storage and maintenance, including security issues and the way data flows from one point to another in an over IT architecture. Because raw information is a key resource for most businesses and organizations, data governance is a logical area of overall IT strategy focus for many large enterprises.

Architecture Design

Our reference architectures are arranged by scenario, with related architectures grouped together. Each architecture includes recommended practices, along with considerations for scalability, availability, manageability, and security.